|
Urban Landuse Change Detection using drone image analysis and hierarchical object-oriented classification
|
Amirhosein Babaeepour   , Asghar Milan * , Saeid Sadeghian |
|
|
|
Abstract: (133 Views) |
With rapid population growth and urbanization, accurate monitoring and evaluation of urban resources through land use/land cover (LULC) change detection has gained increasing significance for effective urban management. This study aims to assess the potential of unmanned aerial vehicle (UAV) imagery for change detection using a rule-based object-oriented classification approach in an urban area. Two datasets were utilized: UAV images captured in 2021 by a SenseFly eBeeX drone over southern Chabahar city, and aerial images acquired in 2013 by an Ultracam XP camera from the same area. A key aspect of the methodology was the implementation of a hierarchical classification model. In this model, all classes were processed using a comprehensive set of features, and a separability matrix was used to design the hierarchical structure. At each node of the hierarchy, optimal features were selected to distinguish between classes. Object-based classification was performed using rule-based analysis of image objects, resulting in the extraction of five land use classes based on statistical thresholds of optimal descriptors. A post-classification processing step was applied to refine the initial results. The quantitative findings showed that the rule-based classification achieved a Kappa coefficient and overall accuracy of 0.90 and 93.31% for the Ultracam imagery, and 0.78 and 83.96% for the UAV orthophoto, respectively. Change detection results revealed that the most significant land cover transformation was from bare land to vegetation (32.843%), while the least change was from buildings to roads (2.34%).
|
Article number: 6 |
| Keywords: Change detection, land use, drone-based photogrammetry, classification, high-resolution images |
|
|
Full-Text [PDF 901 kb]
(65 Downloads)
|
Type of Study: Research |
Subject:
Photo&RS
Received: 2025/05/4
|
|
|
|
|
|
|
| References |
1. Gibril, M. B. A., Kalantar, B., Al-Ruzouq, R., Ueda, N., Saeidi, V., Shanableh, A., ... and Shafri, H. Z. (2020). "Mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle-based images using adaptive multiscale image segmentation and classification." Remote Sensing, Vol. 12, No. 7, 1081. [ DOI:10.3390/rs12071081] 2. Sharma, S. K., Shah, J., Maithani, S., and Mishra, V. (2023). "Object Based Approach for Image Feature Extraction from UAV Data." Proc. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 48, pp. 1907-1913. [ DOI:10.5194/isprs-archives-XLVIII-1-W2-2023-1907-2023] 3. Elamin, A., and El-Rabbany, A. (2022). "UAV-based multi-sensor data fusion for urban land cover mapping using a deep convolutional neural network." Remote Sensing, Vol. 14, No. 17, 4298. [ DOI:10.3390/rs14174298] 4. Ojaghi, S., and Khazai, S. (2018). "Improving the Detection of Object-Oriented Changes in High-Resolution Images based on Random Forest Method in Optimal Features Space." Scientific-Research Quarterly of Geographical Data (SEPEHR), Vol. 26, No. 104, pp. 117-127. doi: 10.22131/sepehr.2018.30522. 5. Han, D., Lee, S. B., Song, M., and Cho, J. S. (2021). "Change detection in unmanned aerial vehicle images for progress monitoring of road construction." Buildings, Vol. 11, No. 4, 150. [ DOI:10.3390/buildings11040150] 6. Fytsilis, A. L., Prokos, A., Koutroumbas, K. D., Michail, D., and Kontoes, C. C. (2016). "A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images." ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 119, pp. 165-186. [ DOI:10.1016/j.isprsjprs.2016.06.001] 7. Khadka, D., Zhang, J., Sharma, A., 2025. Geographic object-based image analysis for landslide identification using machine learning on Google Earth Engine. Environmental Earth Sciences 84 (3), 92. [ DOI:10.1007/s12665-024-12045-8] 8. Mantey, S., and Aduah, M. S. (2021). "Comparison of Object-Based Classifiers and Traditional Pixel-Based Classification Techniques Using Landsat Imagery." Ghana Journal of Technology, Vol. 5, No. 2, pp. 60-69. 9. Richards, J. A. (2022). Remote sensing digital image analysis, Vol. 5, pp. 256-258. Berlin/Heidelberg, Germany: Springer. [ DOI:10.1007/978-3-030-82327-6] 10. Powar, S. K., Panhalkar, S. S., and Patil, A. S. (2022). "An Evaluation of Pixel-based and Object-based Classification Methods for Land Use Land Cover Analysis Using Geoinformatic Techniques." Geomatics and Environmental Engineering, Vol. 16, No. 2, pp. 61-75. [ DOI:10.7494/geom.2022.16.2.61] 11. Ouchra, H., Belangour, A. and Erraissi, A. (2022). "A comparative study on pixel-based classification and object-oriented classification of satellite image." International Journal of Engineering Trends and Technology, 70(8), pp. 206-215. [ DOI:10.14445/22315381/IJETT-V70I8P221] 12. Amini, S., Homayouni, S., Safari, A., & Darvishsefat, A. A. (2018). Object-based classification of hyperspectral data using Random Forest algorithm. Geo-spatial information science, 21(2), 127-138. [ DOI:10.1080/10095020.2017.1399674] 13. Cao, J., Leng, W., Liu, K., Liu, L., He, Z., and Zhu, Y. (2018). "Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models." Remote Sensing, Vol. 10, No. 1, 89. [ DOI:10.3390/rs10010089] 14. Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., ... and Tiede, D. (2014). "Geographic object-based image analysis-towards a new paradigm." ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 87, pp. 180-191. [ DOI:10.1016/j.isprsjprs.2013.09.014] 15. Bayazit, M., Dönmez, C., Berberoglu, S., 2025. Assessing the efficiency of pixel-based and object-based image classification using deep learning in an agricultural Mediterranean plain. Environmental Monitoring and Assessment 197 (2), 1-20. [ DOI:10.1007/s10661-024-13431-2] 16. Goswami, A., Sharma, D., Mathuku, H., Gangadharan, S. M. P., Yadav, C. S., Sahu, S. K., ... and Imran, H. (2022). "Change detection in remote sensing image data comparing algebraic and machine learning methods." Electronics, Vol. 11, No. 3, 431. [ DOI:10.3390/electronics11030431] 17. Arabi, F.A., Malamiri, H.R.G., Shojaei, S., Sarsangi, A., Ferreira, C.S.S. and Kalantari, Z. (2022). "Investigating the ability to identify new constructions in urban areas using images from unmanned aerial vehicles, Google Earth, and Sentinel-2." Remote Sensing, 14(13), pp. 3227. [ DOI:10.3390/rs14133227] 18. Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., and Heynen, M. (2004). "Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information." ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 58, No. 3-4, pp. 239-258. [ DOI:10.1016/j.isprsjprs.2003.10.002] 19. Serpico, S. B., and Bruzzone, L. (2001). "A new search algorithm for feature selection in hyperspectral remote sensing images." IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 7, pp. 1360-1367. [ DOI:10.1109/36.934069] 20. Onyango, C. M., and Marchant, J. A. (2003). "Segmentation of row crop plants from weeds using colour and morphology." Computers and Electronics in Agriculture, Vol. 39, No. 3, pp. 141-155. [ DOI:10.1016/S0168-1699(03)00023-1] 21. Burgan, R. E., and Bartlette, R. A. (1993). Monitoring vegetation greenness with satellite data, Vol. 297. U.S. Department of Agriculture, Forest Service, Intermountain Research Station. [ DOI:10.2737/INT-GTR-297] 22. Gevers, T., and Smeulders, A. W. (2000). "Pictoseek: Combining color and shape invariant features for image retrieval." IEEE Transactions on Image Processing, Vol. 9, No. 1, pp. 102-119. [ DOI:10.1109/83.817602] 23. McNairn, H., and Protz, R. (1993). "Mapping corn residue cover on agricultural fields in Oxford County, Ontario, using Thematic Mapper." Canadian Journal of Remote Sensing, Vol. 19, No. 2, pp. 152-159. [ DOI:10.1080/07038992.1993.10874543] 24. Kaufman, Y. J., and Tanre, D. (1992). "Atmospherically resistant vegetation index (ARVI) for EOS-MODIS." IEEE Transactions on Geoscience and Remote Sensing, Vol. 30, No. 2, pp. 261-270. [ DOI:10.1109/36.134076] 25. Jiang, Y., Wen, X., Xiang, D., Tan, D., Li, Z., Zhang, S. and Wan, Y. (2016). "A change detection approach of high-resolution imagery combined the pre-classification with the post-classification comparison." Proc. 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), IEEE, July 2016, pp. 1-6. [ DOI:10.1109/Agro-Geoinformatics.2016.7577670] 26. Liu, X. and Yang, C. (2013). "A kernel spectral angle mapper algorithm for remote sensing image classification." Proc. 2013 6th International Congress on Image and Signal Processing (CISP), IEEE, December 2013, Vol. 2, pp. 814-818. [ DOI:10.1109/CISP.2013.6745277] |
|
| Send email to the article author |
|
|
|
| Add your comments about this article |
|
|
|
|
|
